جستجوی استاد راهنمای دانشگاهی

Markus Wagnerمشاهده پروفایل

دانشیار

• Optimization Algorithms
• Search-Based Software Engineering
• Renewable Energy Production
• +۳ مورد دیگر

Markus Wagner is an Associate Professor at the Department of Data Science and AI, Monash University. He also serves as an Adjunct Associate Professor at the University of Adelaide and a Visiting Professor at Harbin Institute of Technology. His research spans heuristic optimization algorithms, search-based software engineering, and renewable energy systems. He has been a key figure in sustainability initiatives, including founding the IEEE CIS Task Force on Computational Intelligence in Energy and serving as the first Sustainability Officer at ACM SIGEVO. Education: PhD in Computer Science (University of Adelaide, 2013), Diplom in Computer Science (University of Koblenz-Landau, 2009). Notable awards include the 2024 German IT Security Award and the HUMIES Gold Award. He has attracted over AUD 12M in research funding and led projects like Grid Guru and Australian Research for Global Power System Transformation. Research Interests: Optimizing wave energy converters, algorithm design, and AI-driven sustainability solutions. He has pioneered methods in genetic improvement of software and contributed to smart energy systems through roles at the Monash Energy Institute. Awards: Multiple best paper awards, DECRA Fellowship (2016–2018), and recognition for contributions to evolutionary computation. His work bridges theoretical research with industry applications, funded by Google, Facebook, and defense/mining sectors.

Terrence W.K. Makمشاهده پروفایل

مدرس

• Mathematical Optimization
• Machine Learning
• Energy Systems
• +۵ مورد دیگر

Terrence W.K. Mak is a Lecturer in the Department of Data Science & AI at Monash University, within the Faculty of IT. He is affiliated with the Monash Energy Institute and Monash Data Futures Institute, focusing on interdisciplinary research at the intersection of mathematical optimization, machine learning, and energy systems. His work addresses climate change challenges in the energy sector, such as net-zero emissions and climate-resilient infrastructure. Academically, he holds a PhD from the Australian National University (2018), MPhil and BSc from the Chinese University of Hong Kong (2011, 2009). He has held postdoctoral and research roles at Georgia Tech and the University of Michigan, collaborating extensively with Prof. Pascal Van Hentenryck and Prof. Jimmy Lee. His research emphasizes combining machine learning and optimization to solve energy sector problems, including smart grid technologies, disaster management, and sustainable informatics. Key collaborations include ARPA-E projects (e.g., Grid Optimization Competition), US national laboratories, and industry partners like RTE France and Origin Energy. Recent projects include Grid Guru (AI-driven grid optimization) and consultancy for Origin Energy. He supervises PhD students focusing on deep learning and nonlinear optimization for smart grids.

Muhammad Fermi Pashaمشاهده پروفایل

مدرس ارشد

• Computational Neuroimaging
• Intelligent Network Security
• Digital Health
• +۱۵ مورد دیگر

Muhammad Fermi Pasha is a Senior Lecturer at the School of Information Technology, Monash University, Malaysia. He holds a PhD in Brain-inspired Computing from Universiti Sains Malaysia and has been actively contributing to research and teaching since joining Monash. His academic roles include lecturing and serving as Chief Examiner for courses such as FIT1008, FIT2085, FIT9123, FIT5152, and FIT3175, focusing on computer science, business information systems, and usability. PhD in Brain-inspired Computing, Universiti Sains Malaysia (2010) MSc in Computer Science, Universiti Sains Malaysia (2006) BCompSc (Hons) in Software Engineering, Universiti Sains Malaysia (2003) Dr. Pasha's research spans computational neuroimaging, intelligent network security, digital health, and big data analytics. His work integrates artificial intelligence with healthcare applications, including Alzheimer's diagnosis, mHealth platforms, and secure medical data systems. He emphasizes evolving systems, machine intelligence, and neocortex memory modeling. His projects often involve interdisciplinary collaboration and community engagement. The recent publications highlight a strong trend in AI-driven healthcare solutions, secure data systems, and behavioral analysis using deep learning. His work combines computer vision, natural language processing, and cybersecurity to address real-world challenges in medicine and public health. Themes such as microexpression recognition, EHR clustering, blockchain for IIoT, and flood modeling using CNNs reflect his diverse yet cohesive research vision. Awards for software solutions and research projects as team lead or member (specific names not provided) Dr. Pasha supervises multiple PhD students and leads significant research grants, including projects on microexpression recognition, Alzheimer's prediction, and flood modeling. He collaborates with national and international researchers and contributes to UN Sustainable Development Goals, particularly in health and education. His lab work involves developing intelligent systems for medical and environmental applications. He leads or participates in key research labs and teams focused on AI in healthcare, network security, and sustainable computing. These teams develop frameworks for early disease detection, secure data sharing, and environmental resilience using advanced machine learning and blockchain technologies.

Padmanesan Narasimhanمشاهده پروفایل

مدرس ارشد

• Digital Health Interventions
• Artificial Intelligence in Healthcare
• Health Systems and Informatics
• +۳ مورد دیگر

Dr. Padmanesan Narasimhan is a Senior Lecturer and Clinician at the School of Public Health and Community Medicine, University of New South Wales (UNSW), with expertise in Digital Health Interventions , Artificial Intelligence in Healthcare , and Health Systems and Informatics . His work bridges clinical medicine with technological innovation, focusing on infectious diseases like tuberculosis and non-communicable disease management. As a Fellow of the Australasian Institute of Digital Health and former NIH Fogarty Ellison Fellow (2005) , he leads globally recognized research initiatives such as TRIBOT (AI-powered emergency department triage chatbot) and EPIWATCH (AI-driven epidemic surveillance platform). His contributions include securing $5.3 million in funding for EPIWATCH and advocating for digital health integration in underserved communities. Education : MBBS, AMC, MPH and PhD from UNSW, Grad Cert.Univ. Learning and Teaching (UNSW) Research Trends : His 15 most recent publications (2025-2021) emphasize AI applications in public health, leveraging machine learning for epidemic intelligence and digital platforms to connect formal/informal healthcare providers. Key themes include tuberculosis care systems, mobile health for blood pressure control in Nepal, and social enterprise models for integrated healthcare delivery. Articles span Public Health , Infectious Diseases , and Digital Health , with sub-fields addressing mHealth , Health System Innovation , and Global Disease Management . Scientific Awards : NIH Fogarty Ellison Fellow (2005) UNSW Medicine Prize - Outstanding International Advancement (2016) PLuS Alliance Fellow (2019-2022) Fellow, Australasian Institute of Digital Health (2025-) Leadership : Serves as Assistant Director of the WHO Collaborating Centre for eHealth at UNSW, with international training initiatives in digital health and epidemiology. Supervises PhD candidates in topics like Conversational Chatbots in Emergency Triage and AI-Enabled Healthcare Systems .

Jiyang Jiangمشاهده پروفایل

مدرس

• Neuroimaging
• Functional Neuroimaging
• Dementia
• +۸ مورد دیگر

Dr. Jiyang Jiang is a Lecturer and Research Fellow at the Centre for Healthy Brain Ageing within UNSW Psychiatry and Mental Health at the University of New South Wales. His academic career bridges engineering and neuroscience, with a focus on neuroimaging techniques and their application to understanding brain aging and dementia. Current position: Lecturer/Research Fellow Institution: University of New South Wales Department: School of Psychiatry Research center: Centre for Healthy Brain Ageing (CHeBA) Dr. Jiang's research primarily focuses on cerebrovascular disease, vascular cognitive impairment and dementia, and vascular contributions to Alzheimer's Disease. His work applies novel MRI techniques and machine learning approaches to understand the mechanisms of brain vascular lesions. His key research interests include cerebrovascular lesions in ageing, lesion segmentations using machine learning techniques (convolutional neural networks, recurrent neural networks), imaging biomarkers for vascular cognitive impairment and dementia, and brain structural and functional changes in ageing. Analysis of Dr. Jiang's recent publications shows a strong emphasis on neuroimaging biomarkers, particularly white matter hyperintensities, and their relationship to cognitive decline. His work increasingly incorporates machine learning and deep learning techniques for image analysis, while maintaining a strong foundation in population-based studies examining vascular contributions to dementia across diverse cohorts. 2023-2025: William H. Gates Sr. Fellowship from the AD Data Initiative 2020: Centre for Healthy Brain Ageing Publication Award (Early Career category) 2019: Dementia Centre for Research Collaboration Travel Award 2015: Chinese Government Award for Outstanding Student Abroad Dr. Jiang actively supervises multiple PhD students working on neuroimaging, machine learning applications in medical image processing, cerebrovascular diseases, and brain aging and dementias. He has secured research funding including the William H. Gates Sr. Fellowship. His software contributions include the CHeBA Neuroimaging Software (CNS), UBO Detector (a machine learning-based pipeline for white matter hyperintensity segmentation), and TOPMAL (TOolbox for Probabilistic MApping of Lesions). Dr. Jiang is an active member of the research community, serving as a Review Editor for Frontiers in Aging Neuroscience, Guest Editor for Frontiers in Psychiatry, and as a peer reviewer for numerous scientific journals including Neuroimage, Human Brain Mapping, and Neurobiology of Aging. He is also a member of several professional organizations including the Organization for Human Brain Mapping, Society for Neuroscience, International Society for Magnetic Resonance in Medicine, and the Alzheimer's Association.

• Wireless Communications
• IoT
• Machine Learning
• +۷ مورد دیگر

Professor Yonghui Li is a faculty member at the University of Sydney, holding the position of Professor and Director of the Wireless Engineering Laboratory within the School of Electrical and Information Engineering. His research focuses on cutting-edge areas such as wireless communications, IoT, 5G/6G networks, machine learning, and signal processing. He has authored over 400 publications, including a book on full-duplex communications, and secured over $8 million in competitive research funding. Notably, he was awarded the Australian Research Council (ARC) Future Fellowship (2012) and IEEE Fellow (2015). Education: PhD in Telecommunications (2002) Roles: Editor for IEEE Transactions on Communications, Specialty Chief Editor of Frontiers Signal Processing Journal Key Projects: Multi-gigabit mm-wave cellular networks, machine learning for wireless spectrum management, 5G ultra-reliable low-latency systems His research has led to high-impact outcomes, including ISI high-cited papers and ESI Web of Science recognition. He also pioneered the IoT undergraduate program at the University of Sydney. Awards: IEEE Best Paper Awards (ICC 2014, PIMRC 2017), ARC Fellowships Grants: $500M national 'Smart Grid Smart City' project participation Current projects explore 3D beamforming, non-orthogonal multiple access, and physical-layer security in 5G systems. His work bridges theory and practice, with prototypes developed for mm-wave networks and AI-driven spectrum management.

• Privacy-Enhancing Technologies
• Cybersecurity
• Internet of Things (IoT)
• +۵ مورد دیگر

Dr. Imdad Ullah is a Lecturer at the School of Computer Science, The University of Sydney. He holds a PhD in Computer Science & Engineering from UNSW Sydney and has held research positions at Data61 CSIRO Australia, TU Darmstadt (Germany via Alexander von Humboldt Fellowship), and SLAC National Accelerator Laboratory. His expertise spans privacy-enhancing technologies, IoT security, blockchain, and machine learning applications in cybersecurity. **Education:** PhD in Computer Science & Engineering, UNSW Sydney (2012-2016) Bachelor/Master qualifications not explicitly listed **Research Focus:** Privacy-preserving systems for mobile advertising and IoT Blockchain frameworks for edge/fog computing environments Machine learning-driven intrusion detection systems Secure healthcare frameworks using federated learning His work emphasizes interdisciplinary collaboration, including global networks like TEIN and IEEE. **Awards:** Alexander von Humboldt Research Fellowship (2014) UNSW Tuition Fee Scholarship (2012-2016) **Teaching & Leadership:** Teaches courses in machine learning, cybersecurity, and IT strategy IEEE SPARK Coordinator (2024-2026) Member of curriculum design and exam evaluation committees **Funding:** Secured AUD millions in grants for projects like blockchain-IoT security frameworks and privacy-preserving mobile systems.

Sudipta Boseمشاهده پروفایل

مدرس ارشد

• Capital Market
• Cost of Equity Capital
• Carbon Emissions Disclosures and Assurance
• +۷ مورد دیگر

Dr. Sudipta Bose is a Senior Lecturer in Accounting at the University of Newcastle's Newcastle Business School. He holds a PhD in Accounting from UNSW Sydney and is a Fellow of the Association of Commonwealth Universities (UK). His research focuses on capital markets, carbon emissions disclosures, sustainability accounting, and the intersection of accounting with climate change. He has published in top-tier journals such as Journal of Corporate Finance , British Accounting Review , and Accounting, Auditing & Accountability Journal . His work has influenced policy bodies like the World Bank and United Nations. Educational Background: PhD in Accounting, University of New South Wales (UNSW Sydney) MBA in Accounting & Information Systems, University of Dhaka BBA (Hons) in Accounting & Information Systems, University of Dhaka Research Interests: Dr. Bose explores sustainability reporting quality, corporate governance, and machine learning applications in accounting. His recent studies examine climate change disclosures, ESG metrics, and the role of institutional investors in driving accountability. He collaborates with organizations like the Australian Accounting Standards Board (AASB) on regulatory frameworks for greenhouse gas reporting. Awards & Recognition: 2023 CHSF 'Excellence in Research Supervision' Award 2019 Finalist for University of Newcastle 'Teaching Excellence' Award Inaugural Deputy Vice-Chancellor (Academic)'s Merit List for Teaching (2018) Teaching & Supervision: Dr. Bose teaches courses in financial and management accounting. He supervises multiple PhD students and has secured grants from bodies like AASB and the Association of Commonwealth Universities. His research emphasizes actionable insights for policymakers and industry, addressing challenges in sustainable finance and climate-related financial disclosures. Professional Experience: Prior to academia, he worked in corporate finance roles at Telenor Group (Norway) and Australian firms. He is a Chartered Accountant (CA) and Certified Management Accountant (CMA).

Liz Ritchie-Tyoمشاهده پروفایل

استاد

• Tropical Cyclones
• Tropical Meteorology
• Extreme Weather
• +۱۲ مورد دیگر

Liz Ritchie-Tyo is a Professor in the School of Earth, Atmosphere & Environment and the Department of Civil Engineering at Monash University, where she conducts cutting-edge research on tropical cyclones, extreme weather, and climate impacts. She is actively involved in interdisciplinary research, supervision of PhD students, and leadership in national and international scientific organizations. Education: PhD in Atmospheric Science, Monash University (awarded 1 October 1995), focusing on mesoscale aspects of tropical cyclone genesis. Her research spans both fundamental and applied domains, including the physical understanding of tropical cyclogenesis, intensity change, extratropical transition, and landfall impacts. She also investigates the remote sensing of tropical cyclones and their societal and environmental consequences. Her recent publications reflect strong trends in integrating satellite remote sensing, climate modeling, and public health outcomes, particularly in assessing post-cyclone disease risks and radiative effects of storm clouds. This interdisciplinary approach underscores her contributions to climate science and disaster resilience. Scientific Awards and Honors: Fellow of the American Meteorological Society Prof. Ritchie-Tyo has secured significant research funding from prestigious bodies including the Australian Research Council (ARC), the US Office of Naval Research, the National Science Foundation, NOAA, NASA, and the Australian Bureau of Meteorology. She currently leads multiple projects, such as the ARC Centre of Excellence for the Weather of the 21st Century and a project on the contribution of tropical cyclones to Earth's energy budget. She has supervised numerous students and contributes extensively through editorial roles, including as editor of Weather and Forecasting and former editor of Monthly Weather Review . Research Labs and Teams: She is a key member of collaborative research teams working on tropical cyclone dynamics and climate impacts, including international networks under the World Meteorological Organization (WMO) and partnerships with institutions in the US and Australia. Her work is highly collaborative, involving experts in meteorology, oceanography, public health, and remote sensing.

Eugenia Gasparriمشاهده پروفایل

مدرس ارشد

• Sustainable Construction
• Architecture
• High-Performance Building Envelopes
• +۴ مورد دیگر

Dr. Eugenia Gasparri is a Senior Lecturer in Architectural Technologies at the University of Sydney's School of Architecture, Design and Planning. She serves as Associate Dean (Research Education) and Stream Director for Sustainable Design in the Master of Architectural Science program. A Fellow of the Higher Education Academy (FHEA) and former co-chair of the University of Sydney Pride Network (Gold AWAI Awards 2022–2024), she holds a PhD in Building Engineering with honors from Politecnico di Milano. Educations : MEng, PhD in Building Engineering (Politecnico di Milano), FHEA Her research focuses on sustainable construction through multi-disciplinary innovation, particularly in high-performance facades and industrialized timber technologies . She develops strategies to reduce environmental impact, enhance construction productivity, and promote circular approaches. Her work bridges academia and industry, serving as scientific advisor for national and international projects. Recent publications highlight trends in cross-laminated timber systems , carbon emission assessment , and agrivoltaics integration into building envelopes. These works emphasize lifecycle assessment, moisture management, and machine learning applications for sustainable design. Scientific Awards : Gold AWAI Awards (2022–2024), Fellowship of the Higher Education Academy (FHEA) Dr. Gasparri supervises research students working on industrialized timber façades , AI-powered design frameworks , and mold resilience in building envelopes. She has secured grants including the 2024 Ignition Grant for Timber Active Envelopes, 2023 ARC Research Hub for Timber in Australian Built Environment, and 2022 Victorian Building Authority Research Grant. As a member of the Sydney Environment Institute, Net Zero Institute, and Nano Institute, she contributes to multidisciplinary research through her timber envelope lab and collaborations with international teams.

• Big Data
• Social Network Mining
• Stream Processing
• +۱۲ مورد دیگر

Dr. Thanh Tam Nguyen is a Lecturer at the School of Information and Communication Technology, Griffith University, Gold Coast Campus. He holds a PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and is a Fellow of the Higher Education Academy (FHEA). His academic and research profile is centered on advancing Big Data and Smart technologies through efficient and trustworthy AI systems. His educational background includes a Doctor of Science and a Master of Science, both from EPFL. He is actively involved in high-impact research and has published over 65 papers in top-tier venues such as SIGMOD, VLDB, SIGIR, ICDE, IJCAI, VLDBJ, TKDE, and Pattern Recognition, with over 35 in CORE A* journals and conferences. His work has attracted more than 4,200 citations (h-index 35+). Dr. Nguyen's research focuses on Big Data Analytics, Social Network Mining, Stream Processing, Privacy-Preserving Machine Learning, Recommender Systems, Explainable AI, and Graph Neural Networks . He aims to bridge human insights with data models to ensure transparency and trust in data-driven decisions. His recent work explores misinformation management, machine unlearning, and federated learning, with applications in social networks, healthcare, and sustainable agriculture. The 15 most recent publications reflect a strong trend in privacy-preserving AI, explainability, federated learning, and graph-based modeling . Key themes include machine unlearning, adversarial robustness in recommender systems, heterogeneous graph representations, and trustworthy AI deployment. His work is frequently published in ACM and IEEE venues, indicating sustained excellence in computer science and AI research. Fellow of the Higher Education Academy (FHEA) Dr. Nguyen has secured over $1 million in research funding from government (DFAT, A4I, AKF), industry (CSIRO, Ubitech, Johnson & Johnson), and international bodies (NAFOSTED, ETRI, KARI). He serves as a guest editor for IEEE Journal of Biomedical and Health Informatics, area chair for ACL and EMNLP, and reviewer for top journals like TKDE, JVLDB, and CSUR. He supervises multiple PhD students and collaborates with leading international researchers such as Prof. Karl Aberer (EPFL), Prof. Björn Schuller (Imperial College), and Prof. Hongzhi Yin (UQ). He is a key contributor to the Responsible Big Data Lab at Griffith University and leads projects on misinformation management, AI safety, and sustainable agriculture through AI-powered traceability. His impact extends to policy and industry, particularly in cybersecurity and trustworthy AI adoption.

Felipe Werndl Trevizanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Automated Planning and Scheduling
• Reasoning under Uncertainty
• +۹ مورد دیگر

Felipe Werndl Trevizan is a Senior Lecturer in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of Artificial Intelligence, Operations Research, and Machine Learning. His work focuses on automated planning and scheduling, reasoning under uncertainty, heuristic search, and machine learning for planning. He is actively involved in research projects related to uncertainty-centric pilot assistance systems and probabilistic planning. His research interests include: Automated Planning and Scheduling Probabilistic and Multi-Objective Planning Heuristic Search and Admissible Heuristics Deep Learning for Generalized Planning Constraint-Based and Risk-Aware Planning Learning Heuristics from Data His recent publications demonstrate a strong trend toward integrating machine learning with classical planning techniques, particularly in learning reliable, domain-independent heuristics using both deep and classical machine learning methods. He has developed novel algorithms for constrained and stochastic shortest path problems, multi-objective probabilistic planning, and generalized planning using neural networks. His scientific awards include: Best Paper Award at ICAPS 2016 Best Paper Award at ICAPS 2017 2016 Kikuchi-Karlaftis Best Paper Award from the Transport Research Board Felipe Werndl Trevizan is registered to supervise research students and has advised several successful projects in AI planning. He has secured research grants for projects such as 'Integrated Planning for Uncertainty-Centric Pilot Assistance Systems' and 'Chance-Constrained POMDP for Pilot Assistance Systems' at ANU. He is also organizing key community events including the 'Reliability In Planning and Learning (RIPL)' workshop and a tutorial on 'Learning for Planning' at ICAPS 2025. He is affiliated with the School of Computing at ANU and previously worked as a Senior Research Scientist at NICTA and Data61/CSIRO. His research is supported by active collaborations with leading researchers such as Sylvie Thiébaux, Hanna Kurniawati, and others.

• Agricultural Economics
• Food Security
• Commodity Markets
• +۴ مورد دیگر

Oleg Nivievskyi is a prominent agricultural economist with dual institutional affiliations, primarily at Kyiv School of Economics (90%) and secondarily at the School of Economics, University of Queensland (10%). His research focuses on agricultural economics, food security, and commodity markets with particular emphasis on Ukraine and the Black Sea region. He holds a terminal degree from Georg-August-Universität Göttingen obtained in 2010. Dr. Nivievskyi's research interests span agricultural policy analysis, spatial price transmission, crop rotation economics, and the economic impacts of conflict on agricultural systems. His recent work has been dominated by studies examining the global ramifications of Russia's invasion of Ukraine on agricultural markets, food security, and trade patterns. He specializes in quantitative analysis of market dynamics, policy impacts, and agricultural productivity, often employing sophisticated modeling techniques including spatial price equilibrium modeling and machine learning applications. His publication record reveals a strong focus on the intersection of agricultural economics and geopolitical events, particularly the war in Ukraine. The research trends indicate increasing sophistication in modeling approaches, moving from traditional agricultural economics analyses to more complex network equilibrium models that account for wartime disruptions to global food systems. His work spans both theoretical economic modeling and practical policy analysis, with significant attention to Ukraine's agricultural sector and its role in global food security. Dr. Nivievskyi maintains active research collaborations across multiple institutions, including frequent co-authorship with researchers from the University of Göttingen, World Bank, and various Ukrainian research entities. His work appears in leading agricultural economics journals and has been cited by numerous researchers studying commodity markets, food security, and the economic impacts of conflict. While specific details about his teaching responsibilities and graduate student supervision aren't explicitly documented in the provided materials, his extensive publication record and institutional affiliations suggest active engagement in academic mentoring and research leadership. His work has received support from major international organizations including the World Bank and International Agricultural Trade Research Consortium.

Dana Rezazadeganمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Dana Rezazadegan is a Senior Lecturer in Computer Science and Software Engineering at Swinburne University of Technology, affiliated with the School of Science, Computing and Emerging Technologies. Her work bridges artificial intelligence, computer vision, and healthcare applications, with a strong focus on human-robot collaboration and digital health solutions. Research Interests: Her primary research areas include deep learning, computer vision, LLMs, health informatics, continual learning, and AI for inclusive education. She investigates how intelligent systems can support human activities in healthcare and assistive robotics. The recent publications highlight a consistent focus on applying AI to healthcare challenges—ranging from clinical NLP and patient decision support to automated severity assessment in diseases like COVID-19. She also contributes significantly to foundational AI methods, including uncertainty quantification, speech summarization, and real-time 3D pose estimation for embedded and mobile platforms. Superstar of STEM, Science and Technology Australia, 2021 Teaching Award, Swinburne University of Technology, 2020 Early Career Marie Curie Fellowship, 2014 Top 20 in Iran's Mathematics Olympiad, 2000 Dana actively supervises PhD students on topics such as continual learning, AI tutoring, electricity price forecasting, and 3D pose estimation. She has secured multiple research grants, including projects on inclusive AI for dyslexic learners and AI for energy forecasting. Her collaborative work spans institutions like Macquarie University and CSIRO, and she engages in public outreach through media contributions on AI ethics and privacy. She leads and contributes to interdisciplinary teams focused on AI in healthcare and education, often involving co-design with clinicians and end-users. Her labs and research groups emphasize real-world applicability, ethical considerations, and human-centered AI design.

• Robotics
• Space Robotics
• Collaborative Robotics
• +۱۰ مورد دیگر

Dr. Michelle Dunn is a Senior Lecturer in the School of Engineering at Swinburne University of Technology, where she leads research in robotics and signal processing. She serves as the academic lead for the Swinburne Rover Team and the lead of Space Robotics within the Swinburne Space Technology and Industry Institute. Her interdisciplinary work bridges engineering, space technology, industrial monitoring, and cultural heritage conservation. School: School of Engineering University: Swinburne University of Technology Academic Rank: Senior Lecturer Dr. Dunn's research is centered on robotics and signal processing, with key interests in Space Robotics, Collaborative Robotics, Lunar Dust Mitigation, and Assistive Technologies. She applies signal analysis techniques to diverse fields including steelmaking, where she uses acoustic and vibration signals to monitor processes, and art conservation, where she integrates spectroscopy and imaging to analyze paintings. Her work combines experimental modeling, sensor development, and data analytics to solve real-world engineering challenges. Her recent publications span robotics in agriculture, lunar dust mitigation, additive manufacturing defect detection, and acoustic monitoring in steelmaking. These works reflect a strong trend toward applying robotics and advanced signal processing to practical, interdisciplinary problems. She frequently collaborates with industry and government agencies, securing funding from the ARC and Department of Industry, Science and Resources. Scientific awards include: Vice-Chancellor's Teaching Excellence (Higher Education) Award (2018) Vice-Chancellor's Teaching Excellence (Higher Education) Award (2013) Dr. Dunn actively supervises PhD and Master’s students across diverse projects, including heat flow monitoring, assistive mobility devices, UAV-based parking systems, and EEG-based voice detection. She has been principal investigator on multiple ARC-funded grants, such as the ARC Training Centre for Collaborative Robotics in Advanced Manufacturing and projects focused on sound and vibration monitoring in steelmaking. Her research contracts include development of portable parasite detection systems and cuffless blood pressure monitoring. She leads and contributes to laboratory-based research in the Swinburne Space Technology and Industry Institute, focusing on space robotics and lunar dust mitigation. Her team also operates experimental setups for acoustic and vibration monitoring in industrial models, supporting innovation in steelmaking and manufacturing.